Alternatives to Loggly logo

Alternatives to Loggly

Splunk, Kibana, Graylog, Elasticsearch, and New Relic are the most popular alternatives and competitors to Loggly.
207
111
+ 1
167

What is Loggly and what are its top alternatives?

The world's most popular cloud-based log management service delivers application intelligence.
Loggly is a tool in the Log Management category of a tech stack.

Loggly alternatives & related posts

Splunk logo

Splunk

134
75
0
134
75
+ 1
0
Search, monitor, analyze and visualize machine data
    Be the first to leave a pro
    Splunk logo
    Splunk
    VS
    Loggly logo
    Loggly

    related Splunk posts

    Grafana
    Grafana
    Splunk
    Splunk
    Kibana
    Kibana

    I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

    See more

    related Kibana posts

    Tymoteusz Paul
    Tymoteusz Paul
    Devops guy at X20X Development LTD · | 12 upvotes · 196.6K views
    Amazon EC2
    Amazon EC2
    LXC
    LXC
    CircleCI
    CircleCI
    Docker
    Docker
    Git
    Git
    Vault
    Vault
    Apache Maven
    Apache Maven
    Slack
    Slack
    Jenkins
    Jenkins
    TeamCity
    TeamCity
    Logstash
    Logstash
    Kibana
    Kibana
    Elasticsearch
    Elasticsearch
    Ansible
    Ansible
    VirtualBox
    VirtualBox
    Vagrant
    Vagrant

    Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

    It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

    I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

    We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

    If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

    The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

    Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

    See more
    Tanya Bragin
    Tanya Bragin
    Product Lead, Observability at Elastic · | 10 upvotes · 34.6K views
    atElasticElastic
    Kibana
    Kibana
    Logstash
    Logstash
    Elasticsearch
    Elasticsearch

    ELK Stack (Elasticsearch, Logstash, Kibana) is widely known as the de facto way to centralize logs from operational systems. The assumption is that Elasticsearch (a "search engine") is a good place to put text-based logs for the purposes of free-text search. And indeed, simply searching text-based logs for the word "error" or filtering logs based on a set of a well-known tags is extremely powerful, and is often where most users start.

    See more

    related Elasticsearch posts

    Julien DeFrance
    Julien DeFrance
    Full Stack Engineering Manager at ValiMail · | 16 upvotes · 269.3K views
    atSmartZipSmartZip
    Amazon DynamoDB
    Amazon DynamoDB
    Ruby
    Ruby
    Node.js
    Node.js
    AWS Lambda
    AWS Lambda
    New Relic
    New Relic
    Amazon Elasticsearch Service
    Amazon Elasticsearch Service
    Elasticsearch
    Elasticsearch
    Superset
    Superset
    Amazon Quicksight
    Amazon Quicksight
    Amazon Redshift
    Amazon Redshift
    Zapier
    Zapier
    Segment
    Segment
    Amazon CloudFront
    Amazon CloudFront
    Memcached
    Memcached
    Amazon ElastiCache
    Amazon ElastiCache
    Amazon RDS for Aurora
    Amazon RDS for Aurora
    MySQL
    MySQL
    Amazon RDS
    Amazon RDS
    Amazon S3
    Amazon S3
    Docker
    Docker
    Capistrano
    Capistrano
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk
    Rails API
    Rails API
    Rails
    Rails
    Algolia
    Algolia

    Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

    I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

    For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

    Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

    Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

    Future improvements / technology decisions included:

    Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

    As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

    One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

    See more
    Tim Specht
    Tim Specht
    ‎Co-Founder and CTO at Dubsmash · | 16 upvotes · 52.4K views
    atDubsmashDubsmash
    Memcached
    Memcached
    Algolia
    Algolia
    Elasticsearch
    Elasticsearch
    #SearchAsAService

    Although we were using Elasticsearch in the beginning to power our in-app search, we moved this part of our processing over to Algolia a couple of months ago; this has proven to be a fantastic choice, letting us build search-related features with more confidence and speed.

    Elasticsearch is only used for searching in internal tooling nowadays; hosting and running it reliably has been a task that took up too much time for us in the past and fine-tuning the results to reach a great user-experience was also never an easy task for us. With Algolia we can flexibly change ranking methods on the fly and can instead focus our time on fine-tuning the experience within our app.

    Memcached is used in front of most of the API endpoints to cache responses in order to speed up response times and reduce server-costs on our side.

    #SearchAsAService

    See more
    New Relic logo

    New Relic

    14.4K
    2.9K
    1.9K
    14.4K
    2.9K
    + 1
    1.9K
    SaaS Application Performance Management for Ruby, PHP, .Net, Java, Python, and Node.js Apps.
    New Relic logo
    New Relic
    VS
    Loggly logo
    Loggly

    related New Relic posts

    Sebastian Gębski
    Sebastian Gębski
    CTO at Shedul/Fresha · | 4 upvotes · 223.6K views
    atFresha EngineeringFresha Engineering
    Logentries
    Logentries
    Sentry
    Sentry
    AppSignal
    AppSignal
    New Relic
    New Relic
    GitHub
    GitHub
    Git
    Git
    Jenkins
    Jenkins
    CircleCI
    CircleCI

    Regarding Continuous Integration - we've started with something very easy to set up - CircleCI , but with time we're adding more & more complex pipelines - we use Jenkins to configure & run those. It's much more effort, but at some point we had to pay for the flexibility we expected. Our source code version control is Git (which probably doesn't require a rationale these days) and we keep repos in GitHub - since the very beginning & we never considered moving out. Our primary monitoring these days is in New Relic (Ruby & SPA apps) and AppSignal (Elixir apps) - we're considering unifying it in New Relic , but this will require some improvements in Elixir app observability. For error reporting we use Sentry (a very popular choice in this class) & we collect our distributed logs using Logentries (to avoid semi-manual handling here).

    See more
    Julien DeFrance
    Julien DeFrance
    Full Stack Engineering Manager at ValiMail · | 3 upvotes · 44K views
    atStessaStessa
    Datadog
    Datadog
    New Relic
    New Relic
    #APM

    Which #APM / #Infrastructure #Monitoring solution to use?

    The 2 major players in that space are New Relic and Datadog Both are very comparable in terms of pricing, capabilities (Datadog recently introduced APM as well).

    In our use case, keeping the number of tools minimal was a major selection criteria.

    As we were already using #NewRelic, my recommendation was to move to the pro tier so we would benefit from advanced APM features, synthetics, mobile & infrastructure monitoring. And gain 360 degree view of our infrastructure.

    Few things I liked about New Relic: - Mobile App and push notificatin - Ease of setting up new alerts - Being notified via email and push notifications without requiring another alerting 3rd party solution

    I've certainly seen use cases where NewRelic can also be used as an input data source for Datadog. Therefore depending on your use case, it might also be worth evaluating a joint usage of both solutions.

    See more
    Logstash logo

    Logstash

    2.7K
    1.8K
    95
    2.7K
    1.8K
    + 1
    95
    Collect, Parse, & Enrich Data
    Logstash logo
    Logstash
    VS
    Loggly logo
    Loggly

    related Logstash posts

    Tymoteusz Paul
    Tymoteusz Paul
    Devops guy at X20X Development LTD · | 12 upvotes · 196.6K views
    Amazon EC2
    Amazon EC2
    LXC
    LXC
    CircleCI
    CircleCI
    Docker
    Docker
    Git
    Git
    Vault
    Vault
    Apache Maven
    Apache Maven
    Slack
    Slack
    Jenkins
    Jenkins
    TeamCity
    TeamCity
    Logstash
    Logstash
    Kibana
    Kibana
    Elasticsearch
    Elasticsearch
    Ansible
    Ansible
    VirtualBox
    VirtualBox
    Vagrant
    Vagrant

    Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

    It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

    I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

    We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

    If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

    The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

    Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

    See more
    Tanya Bragin
    Tanya Bragin
    Product Lead, Observability at Elastic · | 10 upvotes · 34.6K views
    atElasticElastic
    Kibana
    Kibana
    Logstash
    Logstash
    Elasticsearch
    Elasticsearch

    ELK Stack (Elasticsearch, Logstash, Kibana) is widely known as the de facto way to centralize logs from operational systems. The assumption is that Elasticsearch (a "search engine") is a good place to put text-based logs for the purposes of free-text search. And indeed, simply searching text-based logs for the word "error" or filtering logs based on a set of a well-known tags is extremely powerful, and is often where most users start.

    See more
    Logentries logo

    Logentries

    263
    113
    102
    263
    113
    + 1
    102
    Real-time log management and analytics built for the cloud
    Logentries logo
    Logentries
    VS
    Loggly logo
    Loggly

    related Logentries posts

    Sumo Logic
    Sumo Logic
    Papertrail
    Papertrail
    Timber.io
    Timber.io
    LogDNA
    LogDNA
    Logentries
    Logentries
    #Heroku

    Logentries, LogDNA, Timber.io, Papertrail and Sumo Logic provide free pricing plan for #Heroku application. You can add these applications as add-ons very easily.

    See more
    ELK logo

    ELK

    177
    86
    0
    177
    86
    + 1
    0
    The acronym for three open source projects: Elasticsearch, Logstash, and Kibana
      Be the first to leave a pro
      ELK logo
      ELK
      VS
      Loggly logo
      Loggly

      related ELK posts

      Wallace Alves
      Wallace Alves
      Cyber Security Analyst · | 1 upvotes · 13.3K views
      nginx
      nginx
      Logstash
      Logstash
      Kibana
      Kibana
      Elasticsearch
      Elasticsearch
      ELK
      ELK
      Portainer
      Portainer
      Docker Compose
      Docker Compose
      Docker
      Docker

      Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

      See more

      related Datadog posts

      Robert Zuber
      Robert Zuber
      CTO at CircleCI · | 8 upvotes · 63.9K views
      atCircleCICircleCI
      Looker
      Looker
      PostgreSQL
      PostgreSQL
      Amplitude
      Amplitude
      Segment
      Segment
      Rollbar
      Rollbar
      Honeycomb
      Honeycomb
      PagerDuty
      PagerDuty
      Datadog
      Datadog

      Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

      We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

      See more
      StackShare Editors
      StackShare Editors
      Flask
      Flask
      AWS EC2
      AWS EC2
      Celery
      Celery
      Datadog
      Datadog
      PagerDuty
      PagerDuty
      Airflow
      Airflow
      StatsD
      StatsD
      Grafana
      Grafana

      Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

      Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

      There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

      Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

      Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

      Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

      See more

      related Papertrail posts

      Sumo Logic
      Sumo Logic
      Papertrail
      Papertrail
      Timber.io
      Timber.io
      LogDNA
      LogDNA
      Logentries
      Logentries
      #Heroku

      Logentries, LogDNA, Timber.io, Papertrail and Sumo Logic provide free pricing plan for #Heroku application. You can add these applications as add-ons very easily.

      See more
      Fluentd logo

      Fluentd

      222
      141
      4
      222
      141
      + 1
      4
      Unified logging layer
      Fluentd logo
      Fluentd
      VS
      Loggly logo
      Loggly
      Sumo Logic logo

      Sumo Logic

      129
      81
      19
      129
      81
      + 1
      19
      Cloud Log Management for Application Logs and IT Log Data
      Sumo Logic logo
      Sumo Logic
      VS
      Loggly logo
      Loggly

      related Sumo Logic posts

      Sumo Logic
      Sumo Logic
      Papertrail
      Papertrail
      Timber.io
      Timber.io
      LogDNA
      LogDNA
      Logentries
      Logentries
      #Heroku

      Logentries, LogDNA, Timber.io, Papertrail and Sumo Logic provide free pricing plan for #Heroku application. You can add these applications as add-ons very easily.

      See more
      AWS CloudTrail logo

      AWS CloudTrail

      126
      80
      14
      126
      80
      + 1
      14
      Record AWS API calls for your account and have log files delivered to you
      AWS CloudTrail logo
      AWS CloudTrail
      VS
      Loggly logo
      Loggly
      Splunk Cloud logo

      Splunk Cloud

      67
      79
      10
      67
      79
      + 1
      10
      Easy and fast way to analyze valuable machine data with the convenience of software as a service (SaaS)
      Splunk Cloud logo
      Splunk Cloud
      VS
      Loggly logo
      Loggly

      related LogDNA posts

      Sumo Logic
      Sumo Logic
      Papertrail
      Papertrail
      Timber.io
      Timber.io
      LogDNA
      LogDNA
      Logentries
      Logentries
      #Heroku

      Logentries, LogDNA, Timber.io, Papertrail and Sumo Logic provide free pricing plan for #Heroku application. You can add these applications as add-ons very easily.

      See more
      Filebeat logo

      Filebeat

      27
      12
      0
      27
      12
      + 1
      0
      A lightweight shipper for forwarding and centralizing log data
        Be the first to leave a pro
        Filebeat logo
        Filebeat
        VS
        Loggly logo
        Loggly
        logz.io logo

        logz.io

        25
        2
        0
        25
        2
        + 1
        0
        A log management and log analysis service
          Be the first to leave a pro
          logz.io logo
          logz.io
          VS
          Loggly logo
          Loggly
          Scalyr logo

          Scalyr

          24
          20
          9
          24
          20
          + 1
          9
          Cloud-based log aggregation, server monitoring, and real-time analysis tool.
          Scalyr logo
          Scalyr
          VS
          Loggly logo
          Loggly
          SLF4J logo

          SLF4J

          17
          7
          0
          17
          7
          + 1
          0
          Simple logging facade for Java
            Be the first to leave a pro
            SLF4J logo
            SLF4J
            VS
            Loggly logo
            Loggly